code: def _load(path, model=None):
      status = KerasQuantizedModel._load_status(path)
      invalidInputError(
          model is not None,
          errMsg="FP32 model is required to create a quantized model.",
      )
      qmodel = TensorflowModel("saved_model", str(path))
      from packaging import version
  
      if version.parse(inc_version) < version.parse("1.11"):
          path = Path(path)
          tune_cfg_file = path / "best_configure.yaml"
          with open(tune_cfg_file, "r") as f:
              tune_cfg = yaml.safe_load(f)
              qmodel.tune_cfg = tune_cfg
      model = KerasQuantizedModel(qmodel)
  
      if os.path.exists(Path(path) / status["compile_path"]):
          with open(Path(path) / status["compile_path"], "rb") as f:
              kwargs = SafePickle.load(f)
              model.compile(**kwargs)
      return model
  
file_path: BigDL/python/nano/src/bigdl/nano/deps/neural_compressor/tensorflow/model.py
imports: import os
  from bigdl.nano.utils.common import SafePickle
  import yaml
  from pathlib import Path
  from typing import Sequence, Any, Union, Dict
  from neural_compressor.model.model import TensorflowModel
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.utils.tf import convert_all
  from bigdl.nano.tf.model import KerasOptimizedModel
  from ..core import version
label_desc: Use image node_id 7 for calling the KerasQuantizedModel obj's underlying member method code with example usage: obj._load(path, model) and returns: model